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Python Deep Learning

Valentino Zocca Gianmario Spacagna Daniel Slater Peter Roelants 著

更新時間：2021-07-02 23:33:02

開會員，本書免費讀 >

ThisbookisforDataSciencepractitionersaswellasaspirantswhohaveabasicfoundationalunderstandingofMachineLearningconceptsandsomeprogrammingexperiencewithPython.Amathematicalbackgroundwithaconceptualunderstandingofcalculusandstatisticsisalsodesired.

目錄(85章)

倒序

封面
版權(quán)信息
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Chapter 1. Machine Learning – An Introduction
What is machine learning?
Different machine learning approaches
Summary
Chapter 2. Neural Networks
Why neural networks?
Fundamentals
Summary
Chapter 3. Deep Learning Fundamentals
What is deep learning?
Deep learning applications
GPU versus CPU
Popular open source libraries – an introduction
Summary
Chapter 4. Unsupervised Feature Learning
Autoencoders
Restricted Boltzmann machines
Summary
Chapter 5. Image Recognition
Similarities between artificial and biological models
Intuition and justification
Convolutional layers
Pooling layers
Dropout
Convolutional layers in deep learning
Convolutional layers in Theano
A convolutional layer example with Keras to recognize digits
A convolutional layer example with Keras for cifar10
Pre-training
Summary
Chapter 6. Recurrent Neural Networks and Language Models
Recurrent neural networks
Language modeling
Speech recognition
Summary
Bibliography
Chapter 7. Deep Learning for Board Games
Early game playing AI
Using the min-max algorithm to value game states
Implementing a Python Tic-Tac-Toe game
Learning a value function
Training AI to master Go
Upper confidence bounds applied to trees
Deep learning in Monte Carlo Tree Search
Quick recap on reinforcement learning
Policy gradients for learning policy functions
Policy gradients in AlphaGo
Summary
Chapter 8. Deep Learning for Computer Games
A supervised learning approach to games
Applying genetic algorithms to playing games
Q-Learning
Q-learning in action
Dynamic games
Atari Breakout
Actor-critic methods
Asynchronous methods
Model-based approaches
Summary
Chapter 9. Anomaly Detection
What is anomaly and outlier detection?
Real-world applications of anomaly detection
Popular shallow machine learning techniques
Anomaly detection using deep auto-encoders
H2O
Examples
Summary
Chapter 10. Building a Production-Ready Intrusion Detection System
What is a data product?
Training
Testing
Model validation
Hyper-parameters tuning
End-to-end evaluation
Deployment
Summary
Index 更新時間：2021-07-02 23:33:02

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Python Deep Learning